Agronomic treatments combined with embryo rescue for rapid generation advancement in tomato speed breeding

Unlike other major crops,little research has been performed on tomato to reduce the generation time for speed breeding.We evaluated several agronomic treatments to reduce the generation time of tomato in the‘M82'(determinate)and‘Moneymaker'(indeterminate)varieties and evaluated the best combination in conjunction with embryo rescue.Five container sizes with volumes of 0.2 L(XS),0.45 L(S),0.8 L(M),1.3 L(L),and6 L(XL),were evaluated in the first experiment under the autumn cycle.We found that plants grown in XL containers exhibited better development and required less time from sowing to anthesis(DSA)and from anthesis to fruit ripening(DAR).In the second experiment,using XL containers in the autumn-winter cycle,we evaluated the effects of cold priming at the cotyledonary stage,water stress,P supplementation,and K supplementation on generation time.Compared to the control,we found that cold priming significantly reduced the number of leaves,plant height to first the inflorescence,and DSA(2.7 d),whereas K supplementation reduced the DAR(8.8 d).In contrast,water stress and P supplementation did not significantly affect any of the measured traits like DAR,DSA or fruit set.To validate these data,in a third experiment with XL containers in the spring-summer cycle,the combination of cold priming and K supplementation was tested,confirming the significant effect of this combination on the reduction of generation time(2.9 d for DSA and 3.9 d for DAR)compared to the control.Embryo rescue during the cell expansion cycle(average of 22.0 d and 23.3 d after anthesis for‘M82'and‘Moneymaker',respectively)allowed the shortening of the generation time by 8.7 d in‘M82'and 11.6 d in‘Moneymaker'compared to the in planta fruit ripening.The combination of agronomic treatments with embryo rescue can effectively increase the number of generations per year from three to four for speed breeding of tomato.

New perspectives and prospects for the next generation of combination therapy in inflammatory bowel disease

This article comments on the letter by Lowell et al,which addresses the next generation of combination therapy for inflammatory bowel disease(IBD).As the understanding of the pathogenesis of IBD continues to improve,treatment strategies are evolving rapidly.The letter examines the current status and future directions of combination therapy for IBD,focusing on approaches that combine biologics with immunomodulators and the emerging dual-biologic therapy(DBT).The traditional combination of biologics and immunomodulators has demonstra-ted preliminary efficacy by enhancing the effects of biologics through immunomo-dulation.However,concerns regarding long-term safety warrant careful evalua-tion.Recent trials,including DUET-Crohn's disease and DUET-ulcerative colitis,have shown promising potential for the broader adoption of DBT.Nevertheless,comprehensive data on efficacy and safety,as well as the effective integration of supportive treatments,remain essential to establish new paradigms for the next generation of IBD care.

Difference between grid connections of large-scale wind power and conventional synchronous generation

In China, regions with abundant wind energy resources are generally located at the end of power grids. The power grid architecture in these regions is typically not sufficiently strong, and the energy structure is relatively simple. Thus, connecting large-capacity wind power units complicates the peak load regulation and stable operation of the power grids in these regions. Most wind turbines use power electronic converter technology, which affects the safety and stability of the power grid differently compared with conventional synchronous generators. Furthermore, fluctuations in wind power cause fluctuations in the output of wind farms, making it difficult to create and implement suitable power generation plans for wind farms. The generation technology and grid connection scheme for wind power and conventional thermal power generation differ considerably. Moreover, the active and reactive power control abilities of wind turbines are weaker than those of thermal power units, necessitating additional equipment to control wind turbines. Hence, to address the aforementioned issues with large-scale wind power generation, this study analyzes the differences between the grid connection and collection strategies for wind power bases and thermal power plants. Based on this analysis, the differences in the power control modes of wind power and thermal power are further investigated. Finally, the stability of different control modes is analyzed through simulation. The findings can be beneficial for the planning and development of large-scale wind power generation farms.

H_2 generation kinetics/thermodynamics and hydrolysis mechanism of high-performance La-doped Mg-Ni alloys in Na Cl solution——A large-scale and quick strategy to get hydrogen

In this work,La-doped Mg-Ni multiphase alloys were prepared by resistance melting furnace(RMF)and then modified by high-energy ball milling(HEBM).The hydrolysis H_(2) generation kinetics/thermodynamics of prepared alloys in Na Cl solutions have been investigated with the help of nonlinear and linear fitting by Avrami-Erofeev and Arrhenius equations.Combining the microstructure information before and after hydrolysis and thermodynamics fitting results,the hydrolysis H_(2) generation mechanism based on nucleation&growth has been elaborated.The final H_(2) generation capacities of 0La,5La,10La and 15 La alloys are 677,653,641 and 770 m L·g^(-1)H_(2) in 240 min at291 K,respectively.While,the final H_(2) generation capacities of HEBM 0La,5La,10La and 15 La alloys are 632,824,611 and 653 m L·g^(-1)H_(2) in 20 min at 291 K,respectively.The as-cast 15La alloy and HEMB 5La alloy present the best H_(2) production rates and final H_(2) production capacities,especially the HEBM 5La can rapidly achieve high H_(2) generation capacity(670 and 824 m L·g^(-1)H_(2) )at low temperature(291 K)within short time(5 and 20 min).The difference between the H_(2) generation capacities is mainly originated from the initial nucleation rate of Mg(OH)_(2) and the subsequent processes affected by the microstructures and phase compositions of the hydrolysis alloys.Relative low initial nucleation rate and fully growth of Mg(OH)_(2) nucleus are the premise of high H_(2) generation capacity due to the hydrolysis H_(2) generation process consisted by the nucleation,growth and contacting of Mg(OH)_(2) nucleus.To utilization H_(2) by designing solid state H_(2) generators using optimized Mg-based alloys is expected to be a feasible H_(2) generation strategy at the moment.

Distributed Generation Effects on Large-Scale Distribution Networks

This paper examines the results of the distributed generation penetration in large-scale medium-voltage power distribution networks. The network examined as a study case consists of twenty one lines fed by three power substations. The injected power comes mainly from photovoltaic units. Specifically, the influences of distributed generation on the network branch currents, losses and voltage profile as well as on the short-circuit level at the medium voltage busbars of the infeeding substations are examined according to international and national standards. The arising problems are explored and technical solutions are proposed. This paper is a pilot application as general conclusions concerning the extended distributed generation penetration in real power distribution networks are set out.

A Two-Layer Encoding Learning Swarm Optimizer Based on Frequent Itemsets for Sparse Large-Scale Multi-Objective Optimization

Traditional large-scale multi-objective optimization algorithms(LSMOEAs)encounter difficulties when dealing with sparse large-scale multi-objective optimization problems(SLM-OPs)where most decision variables are zero.As a result,many algorithms use a two-layer encoding approach to optimize binary variable Mask and real variable Dec separately.Nevertheless,existing optimizers often focus on locating non-zero variable posi-tions to optimize the binary variables Mask.However,approxi-mating the sparse distribution of real Pareto optimal solutions does not necessarily mean that the objective function is optimized.In data mining,it is common to mine frequent itemsets appear-ing together in a dataset to reveal the correlation between data.Inspired by this,we propose a novel two-layer encoding learning swarm optimizer based on frequent itemsets(TELSO)to address these SLMOPs.TELSO mined the frequent terms of multiple particles with better target values to find mask combinations that can obtain better objective values for fast convergence.Experi-mental results on five real-world problems and eight benchmark sets demonstrate that TELSO outperforms existing state-of-the-art sparse large-scale multi-objective evolutionary algorithms(SLMOEAs)in terms of performance and convergence speed.

Resonantly enhanced second-and third-harmonic generation in dielectric nonlinear metasurfaces

Nonlinear dielectric metasurfaces provide a promising approach to control and manipulate frequency conversion optical processes at the nanoscale,thus facilitating both advances in fundamental research and the development of new practical applications in photonics,lasing,and sensing.Here,we employ symmetry-broken metasurfaces made of centrosymmetric amorphous silicon for resonantly enhanced second-and third-order nonlinear optical response.Exploiting the rich physics of optical quasi-bound states in the continuum and guided mode resonances,we comprehensively study through rigorous numerical calculations the relative contribution of surface and bulk effects to second-harmonic generation(SHG)and the bulk contribution to third-harmonic generation(THG) from the meta-atoms.Next,we experimentally achieve optical resonances with high quality factors,which greatly boosts light-matter interaction,resulting in about 550 times SHG enhancement and nearly 5000-fold increase of THG.A good agreement between theoretical predictions and experimental measurements is observed.To gain deeper insights into the physics of the investigated nonlinear optical processes,we further numerically study the relation between nonlinear emission and the structural asymmetry of the metasurface and reveal that the generated harmonic signals arising from linear sharp resonances are highly dependent on the asymmetry of the meta-atoms.Our work suggests a fruitful strategy to enhance the harmonic generation and effectively control different orders of harmonics in all-dielectric metasurfaces,enabling the development of efficient active photonic nanodevices.

Assessment of Wet Season Precipitation in the Central United States by the Regional Climate Simulation of the WRFG Member in NARCCAP and Its Relationship with Large-Scale Circulation Biases

Assessment of past-climate simulations of regional climate models(RCMs)is important for understanding the reliability of RCMs when used to project future regional climate.Here,we assess the performance and discuss possible causes of biases in a WRF-based RCM with a grid spacing of 50 km,named WRFG,from the North American Regional Climate Change Assessment Program(NARCCAP)in simulating wet season precipitation over the Central United States for a period when observational data are available.The RCM reproduces key features of the precipitation distribution characteristics during late spring to early summer,although it tends to underestimate the magnitude of precipitation.This dry bias is partially due to the model’s lack of skill in simulating nocturnal precipitation related to the lack of eastward propagating convective systems in the simulation.Inaccuracy in reproducing large-scale circulation and environmental conditions is another contributing factor.The too weak simulated pressure gradient between the Rocky Mountains and the Gulf of Mexico results in weaker southerly winds in between,leading to a reduction of warm moist air transport from the Gulf to the Central Great Plains.The simulated low-level horizontal convergence fields are less favorable for upward motion than in the NARR and hence,for the development of moist convection as well.Therefore,a careful examination of an RCM’s deficiencies and the identification of the source of errors are important when using the RCM to project precipitation changes in future climate scenarios.

In vivo label-free measurement of blood flow velocity symmetry based on dual line scanning third-harmonic generation microscopy excited at the 1700 nm window

Measurement of bloodflow velocity is key to understanding physiology and pathology in vivo.While most measurements are performed at the middle of the blood vessel,little research has been done on characterizing the instantaneous bloodflow velocity distribution.This is mainly due to the lack of measurement technology with high spatial and temporal resolution.Here,we tackle this problem with our recently developed dual-wavelength line-scan third-harmonic generation(THG)imaging technology.Simultaneous acquisition of dual-wavelength THG line-scanning signals enables measurement of bloodflow velocities at two radially symmetric positions in both venules and arterioles in mouse brain in vivo.Our results clearly show that the instantaneous bloodflow velocity is not symmetric under general conditions.

Natural high-porous diatomaceous-earth based self-floating aerogel for efficient solar steam power generation

The application of solar steam generation in seawater desalination is an effective way to solve the shortage of fresh water resources.At present,many kinds of photothermal conversion materials have been developed and used as evaporators in seawater desalination.However,some evaporators need additional thermal insulation or water supply devices to achieve efficient photothermal conversion.In addition,their complex,time consuming and no scalable preparation process,high cost of raw materials and poor salt resistance hinder the practical application of these evaporator.Owing to its distinctive nanoporous structure,diatomite as fossilized single-cells algae diatoms is a promising natural silica-based material for seawater desalination.They are taken from sea and that makes true sense to use them in the sea.Herein,we report the first example of synthesis robust three-dimensional(3D)natural-diatomite composite by assembling polyaniline nanoparticles covered diatomite into the polyvinyl alcohol pre-treated melamine foam frameworks and demonstrate its application as new evaporator for seawater desalination.The porous framework does not only improve the sunlight scattering efficiency,but also offer large network of channels for water transportation.The inherent mechanism behind salt desalination process involves the absorption of water molecules on the surface of the internal silica micro-nano pores,and evaporation under the heat induced by the polyaniline absorbed sunlight.Meanwhile,the metal ions are segregated by many available pores and channels to achieve the self-desalting effect.The developed evaporator possesses the superiority of multi-stage pore structure,strong hydrophilicity,low thermal conductivity,excellent light absorption,fast water transportation and salt-resistant crystallization as well as good durability.The evaporation rate without an additional device is found to be 1.689 kg m^(-2)h^(-1)under 1-Sun irradiation,and the energy conversion efficiency is as high as 95%.This work creates a platform and develops the prospect of employing green and sustainable natural-diatomite composite evaporator for practical applications of seawater desalination.

Multi-objective optimization and evaluation of supercritical CO_(2) Brayton cycle for nuclear power generation

The supercritical CO_(2) Brayton cycle is considered a promising energy conversion system for Generation IV reactors for its simple layout,compact structure,and high cycle efficiency.Mathematical models of four Brayton cycle layouts are developed in this study for different reactors to reduce the cost and increase the thermohydraulic performance of nuclear power generation to promote the commercialization of nuclear energy.Parametric analysis,multi-objective optimizations,and four decision-making methods are applied to obtain each Brayton scheme’s optimal thermohydraulic and economic indexes.Results show that for the same design thermal power scale of reactors,the higher the core’s exit temperature,the better the Brayton cycle’s thermo-economic performance.Among the four-cycle layouts,the recompression cycle(RC)has the best overall performance,followed by the simple recuperation cycle(SR)and the intercooling cycle(IC),and the worst is the reheating cycle(RH).However,RH has the lowest total cost of investment(C_(tot))of$1619.85 million,and IC has the lowest levelized cost of energy(LCOE)of 0.012$/(kWh).The nuclear Brayton cycle system’s overall performance has been improved due to optimization.The performance of the molten salt reactor combined with the intercooling cycle(MSR-IC)scheme has the greatest improvement,with the net output power(W_(net)),thermal efficiencyη_(t),and exergy efficiency(η_(e))improved by 8.58%,8.58%,and 11.21%,respectively.The performance of the lead-cooled fast reactor combined with the simple recuperation cycle scheme was optimized to increase C_(tot) by 27.78%.In comparison,the internal rate of return(IRR)increased by only 7.8%,which is not friendly to investors with limited funds.For the nuclear Brayton cycle,the molten salt reactor combined with the recompression cycle scheme should receive priority,and the gas-cooled fast reactor combined with the reheating cycle scheme should be considered carefully.

Quantitative effect of kerogen type on the hydrocarbon generation potential of Paleogene lacustrine source rocks,Liaohe Western Depression,China

Kerogen types exert a decisive effect on the onset and capacity of hydrocarbon generation of source rocks.Lacustrine source rocks in the Liaohe Western Depression are characterized by thick deposition,high total organic carbon(TOC)content,various kerogen types,and a wide range of thermal maturity.Consequently,their hydrocarbon generation potential and resource estimation can be misinterpreted.In this study,geochemical tests,numerical analysis,hydrocarbon generation kinetics,and basin modeling were integrated to investigate the differential effects of kerogen types on the hydrocarbon generation potential of lacustrine source rocks.Optimized hydrocarbon generation and expulsion(HGE)models of different kerogen types were established quantitatively upon abundant Rock-Eval/TOC/vitrinite reflectance(R_(o))datasets.Three sets of good-excellent source rocks deposited in the fourth(Es4),third(Es3),and first(Es1)members of Paleogene Shahejie Formation,are predominantly types I-II_(1),II_(1)-II_(2),and II-III,respectively.The activation energy of types I-II_(2)kerogen is concentrated(180-230 kcal/mol),whereas that of type III kerogen is widely distributed(150-280 kcal/mol).The original hydrocarbon generation potentials of types I,II_(1),II_(2),and III kerogens are 790,510,270,and 85 mg/g TOC,respectively.The Ro values of the hydrocarbon generation threshold for type I-III source rocks gradually increase from 0.42%to 0.74%,and Ro values of the hydrocarbon expulsion threshold increase from 0.49%to 0.87%.Types I and II_(1)source rocks are characterized by earlier hydrocarbon generation,more rapid hydrocarbon expulsion,and narrower hydrocarbon generation windows than types II_(2)and III source rocks.The kerogen types also affect the HGE history and resource potential.Three types(conventional,tight,and shale oil/gas)and three levels(realistic,expected,and prospective)of hydrocarbon resources of different members in the Liaohe Western Depression are evaluated.Findings suggest that the Es3 member has considerable conventional and unconventional hydrocarbon resources.This study can quantitatively characterize the hydrocarbon generation potential of source rocks with different kerogen types,and facilitate a quick and accurate assessment of hydrocarbon resources,providing strategies for future oil and gas exploration.

Prediction of corrosion rate for friction stir processed WE43 alloy by combining PSO-based virtual sample generation and machine learning

The corrosion rate is a crucial factor that impacts the longevity of materials in different applications.After undergoing friction stir processing(FSP),the refined grain structure leads to a notable decrease in corrosion rate.However,a better understanding of the correlation between the FSP process parameters and the corrosion rate is still lacking.The current study used machine learning to establish the relationship between the corrosion rate and FSP process parameters(rotational speed,traverse speed,and shoulder diameter)for WE43 alloy.The Taguchi L27 design of experiments was used for the experimental analysis.In addition,synthetic data was generated using particle swarm optimization for virtual sample generation(VSG).The application of VSG has led to an increase in the prediction accuracy of machine learning models.A sensitivity analysis was performed using Shapley Additive Explanations to determine the key factors affecting the corrosion rate.The shoulder diameter had a significant impact in comparison to the traverse speed.A graphical user interface(GUI)has been created to predict the corrosion rate using the identified factors.This study focuses on the WE43 alloy,but its findings can also be used to predict the corrosion rate of other magnesium alloys.

Application of DSAPSO Algorithm in Distribution Network Reconfiguration with Distributed Generation

With the current integration of distributed energy resources into the grid,the structure of distribution networks is becoming more complex.This complexity significantly expands the solution space in the optimization process for network reconstruction using intelligent algorithms.Consequently,traditional intelligent algorithms frequently encounter insufficient search accuracy and become trapped in local optima.To tackle this issue,a more advanced particle swarm optimization algorithm is proposed.To address the varying emphases at different stages of the optimization process,a dynamic strategy is implemented to regulate the social and self-learning factors.The Metropolis criterion is introduced into the simulated annealing algorithm to occasionally accept suboptimal solutions,thereby mitigating premature convergence in the population optimization process.The inertia weight is adjusted using the logistic mapping technique to maintain a balance between the algorithm’s global and local search abilities.The incorporation of the Pareto principle involves the consideration of network losses and voltage deviations as objective functions.A fuzzy membership function is employed for selecting the results.Simulation analysis is carried out on the restructuring of the distribution network,using the IEEE-33 node system and the IEEE-69 node system as examples,in conjunction with the integration of distributed energy resources.The findings demonstrate that,in comparison to other intelligent optimization algorithms,the proposed enhanced algorithm demonstrates a shorter convergence time and effectively reduces active power losses within the network.Furthermore,it enhances the amplitude of node voltages,thereby improving the stability of distribution network operations and power supply quality.Additionally,the algorithm exhibits a high level of generality and applicability.

Enhancing Evolutionary Algorithms With Pattern Mining for Sparse Large-Scale Multi-Objective Optimization Problems

Sparse large-scale multi-objective optimization problems(SLMOPs)are common in science and engineering.However,the large-scale problem represents the high dimensionality of the decision space,requiring algorithms to traverse vast expanse with limited computational resources.Furthermore,in the context of sparse,most variables in Pareto optimal solutions are zero,making it difficult for algorithms to identify non-zero variables efficiently.This paper is dedicated to addressing the challenges posed by SLMOPs.To start,we introduce innovative objective functions customized to mine maximum and minimum candidate sets.This substantial enhancement dramatically improves the efficacy of frequent pattern mining.In this way,selecting candidate sets is no longer based on the quantity of nonzero variables they contain but on a higher proportion of nonzero variables within specific dimensions.Additionally,we unveil a novel approach to association rule mining,which delves into the intricate relationships between non-zero variables.This novel methodology aids in identifying sparse distributions that can potentially expedite reductions in the objective function value.We extensively tested our algorithm across eight benchmark problems and four real-world SLMOPs.The results demonstrate that our approach achieves competitive solutions across various challenges.

Harnessing overlapped temperature-salinity gradient in solar-driven interfacial seawater evaporation for efficient steam and electricity generation

Solar-driven interfacial water evaporation(SIWE)offers a superb way to leverage concentrated solar heat to minimize energy dissipation during seawater desalination.It also engenders overlapped temperaturesalinity gradient(TSG)between water-air interface and adjacent seawater,affording opportunities of harnessing electricity.However,the efficiency of conventional SIWE technologies is limited by significant challenges,including salt passivation to hinder evaporation and difficulties in exploiting overlapped TSG simultaneously.Herein,we report self-sustaining hybrid SIWE for not only sustainable seawater desalination but also efficient electricity generation from TSG.It enables spontaneous circulation of salt flux upon seawater evaporation,inducing a self-cleaning evaporative interface without salt passivation for stable steam generation.Meanwhile,this design enables spatial separation and simultaneous utilization of overlapped TSG to enhance electricity generation.These benefits render a remarkable efficiency of90.8%in solar energy utilization,manifesting in co-generation of solar steam at a fast rate of 2.01 kg m^(-2)-h^(-1)and electricity power of 1.91 W m^(-2)with high voltage.Directly interfacing the hybrid SIWE with seawater electrolyzer constructs a system for water-electricity-hydrogen co-generation without external electricity supply.It produces hydrogen at a rapid rate of 1.29 L h^(-1)m^(-2)and freshwater with 22 times lower Na+concentration than the World Health Organization(WHO)threshold.

Highly Aligned Ternary Nanofiber Matrices Loaded with MXene Expedite Regeneration of Volumetric Muscle Loss

Current therapeutic approaches for volumetric muscle loss(VML)face challenges due to limited graft availability and insufficient bioactivities.To overcome these limitations,tissue-engineered scaffolds have emerged as a promising alternative.In this study,we developed aligned ternary nanofibrous matrices comprised of poly(lactide-co-ε-caprolactone)integrated with collagen and Ti_(3)C_(2)T_(x)MXene nanoparticles(NPs)(PCM matrices),and explored their myogenic potential for skeletal muscle tissue regeneration.The PCM matrices demonstrated favorable physicochemical properties,including structural uniformity,alignment,microporosity,and hydrophilicity.In vitro assays revealed that the PCM matrices promoted cellular behaviors and myogenic differentiation of C2C12 myoblasts.Moreover,in vivo experiments demonstrated enhanced muscle remodeling and recovery in mice treated with PCM matrices following VML injury.Mechanistic insights from next-generation sequencing revealed that MXene NPs facilitated protein and ion availability within PCM matrices,leading to elevated intracellular Ca^(2+)levels in myoblasts through the activation of inducible nitric oxide synthase(i NOS)and serum/glucocorticoid regulated kinase 1(SGK1),ultimately promoting myogenic differentiation via the m TOR-AKT pathway.Additionally,upregulated i NOS and increased NO–contributed to myoblast proliferation and fiber fusion,thereby facilitating overall myoblast maturation.These findings underscore the potential of MXene NPs loaded within highly aligned matrices as therapeutic agents to promote skeletal muscle tissue recovery.

Large-scale model testing of high-pressure grouting reinforcement for bedding slope with rapid-setting polyurethane

Bedding slope is a typical heterogeneous slope consisting of different soil/rock layers and is likely to slide along the weakest interface.Conventional slope protection methods for bedding slopes,such as retaining walls,stabilizing piles,and anchors,are time-consuming and labor-and energy-intensive.This study proposes an innovative polymer grout method to improve the bearing capacity and reduce the displacement of bedding slopes.A series of large-scale model tests were carried out to verify the effectiveness of polymer grout in protecting bedding slopes.Specifically,load-displacement relationships and failure patterns were analyzed for different testing slopes with various dosages of polymer.Results show the great potential of polymer grout in improving bearing capacity,reducing settlement,and protecting slopes from being crushed under shearing.The polymer-treated slopes remained structurally intact,while the untreated slope exhibited considerable damage when subjected to loads surpassing the bearing capacity.It is also found that polymer-cemented soils concentrate around the injection pipe,forming a fan-shaped sheet-like structure.This study proves the improvement of polymer grouting for bedding slope treatment and will contribute to the development of a fast method to protect bedding slopes from landslides.

Two stages power generation test of the hot dry rock exploration and production demonstration project in the Gonghe Basin,northeastern Qinghai-Tibet plateau,China

The Hot Dry Rock(HDR)is considered as a clean and renewable energy,poised to significantly contribute to the global energy decarbonization agenda.Many HDR projects worldwide have accumulated valuable experience in efficient drilling and completion,reservoir construction,and fracture simulation.In 2019,China Geological Survey(CGS)initiated a demonstration project of HDR exploration and production in the Gonghe Basin,aiming to overcome the setbacks faced by HDR projects.Over the ensuing four years,the Gonghe HDR project achieved the first power generation in 2021,followed by the second power generation test in 2022.After establishing the primary well group in the initial phase,two directional wells and one branch well were drilled.Noteworthy progress was made in successfully constructing the targeted reservoir,realizing inter-well connectivity,power generation and grid connection,implementing of the real-time micro-seismic monitoring.A closed-loop technical validation of the HDR exploration and production was completed.However,many technical challenges remain in the process of HDR industrialization,such as reservoir fracture network characterization,efficient drilling and completion,multiple fracturing treatment,continuous injection and production,as well as mitigation of induced seismicity and numerical simulation technology.

Online identification and extraction method of regional large-scale adjustable load-aggregation characteristics

This article introduces the concept of load aggregation,which involves a comprehensive analysis of loads to acquire their external characteristics for the purpose of modeling and analyzing power systems.The online identification method is a computer-involved approach for data collection,processing,and system identification,commonly used for adaptive control and prediction.This paper proposes a method for dynamically aggregating large-scale adjustable loads to support high proportions of new energy integration,aiming to study the aggregation characteristics of regional large-scale adjustable loads using online identification techniques and feature extraction methods.The experiment selected 300 central air conditioners as the research subject and analyzed their regulation characteristics,economic efficiency,and comfort.The experimental results show that as the adjustment time of the air conditioner increases from 5 minutes to 35 minutes,the stable adjustment quantity during the adjustment period decreases from 28.46 to 3.57,indicating that air conditioning loads can be controlled over a long period and have better adjustment effects in the short term.Overall,the experimental results of this paper demonstrate that analyzing the aggregation characteristics of regional large-scale adjustable loads using online identification techniques and feature extraction algorithms is effective.